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Found 29 papers, 3 papers with code
Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents
Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark memory capabilities in decision-making agents.
Believable Minecraft Settlements by Means of Decentralised Iterative Planning
Procedural city generation that focuses on believability and adaptability to random terrain is a difficult challenge in the field of Procedural Content Generation (PCG).
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning
Furthermore, we show that the commonly used single-step retrosynthesis benchmark dataset USPTO-50k is insufficient as this evaluation task does not represent model performance and scalability on larger and more diverse datasets.
Two-Memory Reinforcement Learning
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks.
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction
Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found.
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show.
Continuous Episodic Control
Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space.
Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization
At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially observable environments.
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer
Autonomously trained agents that are supposed to play video games reasonably well rely either on fast simulation speeds or heavy parallelization across thousands of machines running concurrently.
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards.
Reliable validation of Reinforcement Learning Benchmarks
Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI as a whole, and a wide variety of games are used as its prominent test problems.
Potential-based Reward Shaping in Sokoban
While previous work has investigated the use of expert knowledge to generate potential functions, in this work, we study whether we can use a search algorithm(A*) to automatically generate a potential function for reward shaping in Sokoban, a well-known planning task.
High-Accuracy Model-Based Reinforcement Learning, a Survey
Deep reinforcement learning has shown remarkable success in the past few years.
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
We note a surprisingly late adoption of deep learning that starts in 2018.
Transfer Learning and Curriculum Learning in Sokoban
In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much planning.
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning
AlphaZero has achieved impressive performance in deep reinforcement learning by utilizing an architecture that combines search and training of a neural network in self-play.
An Analysis of Phenotypic Diversity in Multi-Solution Optimization
More and more, optimization methods are used to find diverse solution sets.
Applications of Artificial Intelligence in Live Action Role-Playing Games (LARP)
Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre.
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey
In recent years, many model-based methods have been introduced to address this challenge.
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning
A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources.
Versatile Black-Box Optimization
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization.
Warm-Start AlphaZero Self-Play Search Enhancements
Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level.
A New Challenge: Approaching Tetris Link with AI
This paper focuses on a new game, Tetris Link, a board game that is still lacking any scientific analysis.
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete.
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations.
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling.
Hyper-Parameter Sweep on AlphaZero General
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
Learning to Plan Chemical Syntheses
We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions.
